DOE Labs to Build Science Clouds

By Michael Feldman

October 13, 2009

Like many organizations that rely on industrial-strength datacenters, the US Department of Energy (DOE) would like to know if cloud computing can make its life easier. To answer that question, the DOE is launching a $32 million program to study how scientific codes can make use of cloud technology. Called Magellan, the program will be funded by the American Recovery and Reinvestment Act (ARRA), with the money to be split equally between the the two DOE centers that will be conducting the work: the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory and the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory.

One of the major questions the study hopes to answer is how well the DOE’s mid-range scientific workloads match up with various cloud architectures and how those architectures could be optimized for HPC applications. Today most public clouds lack the network performance, as well as CPU and memory capacities to handle many HPC codes. The software environment in public clouds also can be at odds with HPC, since little effort has been made to optimize computational performance at the application level. Purpose-built HPC clouds may be the answer, and much of the Magellan effort will be focused on developing these private “science clouds.”

The bigger question, though, is to find out if the cloud model in general is applicable to high performance computing applications used at DOE labs and can offer a cost-effective and flexible approach for researchers. According to ALCF director Pete Beckman, that means getting the best science for the dollar. In a cloud architecture, the virtualization of resources usually translates into better utilization of hardware. In the HPC realm though, virtualization can be a performance killer and utilization is often not the big problem it is in commercial datacenters where hardware is typically undersubscribed. Perhaps of greater interest for HPC users is the ability to fast-track application deployment by taking advantage of the cloud’s ability to encapsulate complete software environments.

“There are a lot users who spend time developing there own software inside their own software stack,” says Beckman. “Getting those running on traditional supercomputers can be quite challenging. In the cloud model, sometimes these people find it easier to bring their software stack with them. That can broaden the community.”

The entire range of DOE scientific codes will be looked at, including energy research, climate modeling, bioinformatics, physics codes, applied math, and computer science research. But the focus will be on those codes that are typically run on HPC capacity clusters, which represent much of the computing infrastructure at DOE labs today. In general, codes that require capability supercomputers such as the Cray XT and the IBM Blue Gene are not considered candidates for cloud environments. This is mainly because large-scale supercomputing apps tend to be tightly coupled, relying on high speed inter-node communication and a non-virtualized software stack for maximum performance.

Most of the program’s $32 million will, in fact, be spent on new cluster systems, which will form the testbed for Magellan. According to NERSC director Kathy Yelick, the cluster hardware will be fairly generic HPC systems, based on Intel Nehalem CPUs and InfiniBand technology. Total compute performance across both sites will be on the order of 100 teraflops. Yelick says there will also be a storage cloud, with a little over a petabyte of capacity. In addition, flash memory technology will be used to optimize performance for data-intensive applications. The NERSC and ALCF clusters will be linked via ESnet, the DOE’s cutting-edge 100 Gbps network. ESnet was also a recipient of ARRA funding, and will be used to facilitate super-speed data transfers between the two sites.

One of the challenges in building a private cloud today is the lack of software standards. However, the Magellan work will employ some of the more popular frameworks that have emerged from the cloud community. Argonne, for instance, will experiment with the Eucalyptus toolkit, an open-source package that is compatible with Amazon Web Services API. The idea is to be able to build a private cloud with the same interface as Amazon EC2.

Apache’s Hadoop and Google’s MapReduce, two related software frameworks that deal with large distributed datasets, will also be evaluated. Like Eucalyptus, Hadoop and MapReduce grew up outside of the HPC world, so currently there’s not much support for them at traditional supercomputing centers. But the notion of large distributed data sets is a feature of many data-intensive scientific codes and is a natural fit for cloud-style computing.

The other aspect of the Magellan effort has to do with experimentation of commercial cloud offerings, such as those from Amazon, Google, and Microsoft. Public clouds, in particular, are attracting a lot of interest due to their ability to offer virtually infinite capacity and elasticity. (Private clouds, because of their smaller size, tend to be seen as fixed resources.) Just as important to the DOE, a public cloud has the allure of offloading the development and maintainence of local infrastructure to someone else.

“Will it be more cost effective for a commercial entity to run a cloud, and presumably make a profit on it, than for the DOE to run their own cloud?” asks Yelick. “That is going to be one of the questions most challenging to answer.”

Some DOE researchers are already giving public clouds a whirl. Argonne’s Jared Wilkening recently tested the feasibility of employing Amazon EC2 to run a metagenomics application (PDF). The BLAST-based code is a nice fit for cloud computing because there is little internal synchronization, therefore it doesn’t rely on high performance interconnects. Nevertheless, the study’s conclusion was that Amazon is significantly more expensive than locally-owned clusters, due mainly to EC2’s inferior CPU hardware and the premium cost associated with on-demand access. Of course, given increased demand for compute-intensive workloads, that could change. Wilkening’s paper was published in Cluster 2009, and slides (PDF) are available on the conference Web site.

The Magellan program is slated to run for two years, with the initial clusters expected to be installed sometime in the next few months. At NERSC, Yelick says the hardware could arrive as early as November, and become operational in December or January. Meanwhile at Argonne, Beckman is already running into researchers who can’t wait to host their codes on the Magellan cloud. “They’re lined up,” he says. “They keep coming down to my office asking when it will be here and how soon they can log in.”

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Microsoft Wants to Speed Quantum Development

December 12, 2017

Quantum computing continues to make headlines in what remains of 2017 as tech giants jockey to establish a pole position in the race toward commercialization of quantum. This week, Microsoft took the next step in advanci Read more…

By Tiffany Trader

ESnet Now Moving More Than 1 Petabyte/wk

December 12, 2017

Optimizing ESnet (Energy Sciences Network), the world's fastest network for science, is an ongoing process. Recently a two-year collaboration by ESnet users – the Petascale DTN Project – achieved its ambitious goal t Read more…

HPC-as-a-Service Finds Toehold in Iceland

December 11, 2017

While high-demand workloads (e.g., bitcoin mining) can overheat data center cooling capabilities, at least one data center infrastructure provider has announced an HPC-as-a-service offering that features 100 percent fre Read more…

By Doug Black

HPE Extreme Performance Solutions

Explore the Origins of Space with COSMOS and Memory-Driven Computing

From the formation of black holes to the origins of space, data is the key to unlocking the secrets of the early universe. Read more…

HPC Iron, Soft, Data, People – It Takes an Ecosystem!

December 11, 2017

Cutting edge advanced computing hardware (aka big iron) does not stand by itself. These computers are the pinnacle of a myriad of technologies that must be carefully woven together by people to create the computational c Read more…

By Alex R. Larzelere

Microsoft Wants to Speed Quantum Development

December 12, 2017

Quantum computing continues to make headlines in what remains of 2017 as tech giants jockey to establish a pole position in the race toward commercialization of Read more…

By Tiffany Trader

HPC Iron, Soft, Data, People – It Takes an Ecosystem!

December 11, 2017

Cutting edge advanced computing hardware (aka big iron) does not stand by itself. These computers are the pinnacle of a myriad of technologies that must be care Read more…

By Alex R. Larzelere

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Microsoft Spins Cycle Computing into Core Azure Product

December 5, 2017

Last August, cloud giant Microsoft acquired HPC cloud orchestration pioneer Cycle Computing. Since then the focus has been on integrating Cycle’s organization Read more…

By John Russell

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

HPE In-Memory Platform Comes to COSMOS

November 30, 2017

Hewlett Packard Enterprise is on a mission to accelerate space research. In August, it sent the first commercial-off-the-shelf HPC system into space for testing Read more…

By Tiffany Trader

SC17 Cluster Competition: Who Won and Why? Results Analyzed and Over-Analyzed

November 28, 2017

Everyone by now knows that Nanyang Technological University of Singapore (NTU) took home the highest LINPACK Award and the Overall Championship from the recently concluded SC17 Student Cluster Competition. We also already know how the teams did in the Highest LINPACK and Highest HPCG competitions, with Nanyang grabbing bragging rights for both benchmarks. Read more…

By Dan Olds

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Leading Solution Providers

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This